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      Early detection of grapevine leafroll disease in a red-berried wine grape cultivar using hyperspectral imaging

      , , ,
      Computers and Electronics in Agriculture
      Elsevier BV

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          A Survey of Outlier Detection Methodologies

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            Development of spectral indices for detecting and identifying plant diseases

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              Hyperspectral remote sensing of plant pigments.

              The dynamics of pigment concentrations are diagnostic of a range of plant physiological properties and processes. This paper appraises the developing technologies and analytical methods for quantifying pigments non-destructively and repeatedly across a range of spatial scales using hyperspectral remote sensing. Progress in deriving predictive relationships between various characteristics and transforms of hyperspectral reflectance data are evaluated and the roles of leaf and canopy radiative transfer models are reviewed. Requirements are identified for more extensive intercomparisons of different approaches and for further work on the strategies for interpreting canopy scale data. The paper examines the prospects for extending research to the wider range of pigments in addition to chlorophyll, testing emerging methods of hyperspectral analysis and exploring the fusion of hyperspectral and LIDAR remote sensing. In spite of these opportunities for further development and the refinement of techniques, current evidence of an expanding range of applications in the ecophysiological, environmental, agricultural, and forestry sciences highlights the growing value of hyperspectral remote sensing of plant pigments.
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                Author and article information

                Journal
                Computers and Electronics in Agriculture
                Computers and Electronics in Agriculture
                Elsevier BV
                01681699
                December 2020
                December 2020
                : 179
                : 105807
                Article
                10.1016/j.compag.2020.105807
                a6e261fe-0e0d-40cf-837e-c38cb1a2f7b2
                © 2020

                https://www.elsevier.com/tdm/userlicense/1.0/

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